llm-catcher 0.3.0

A Python library that uses LLMs to diagnose and explain exceptions

LLM Catcher

LLM Catcher is a Python library that uses Large Language Models to diagnose and explain exceptions in your code.

Features

• Exception diagnosis using LLMs
• Both synchronous and asynchronous APIs

Installation

pip install llm-catcher

Quick Start

1. Create a .env file with your OpenAI API key:

LLM_CATCHER_OPENAI_API_KEY=your-api-key-here

Examples

The examples/ directory contains several examples demonstrating different use cases:

1. Minimal Example (examples/minimal.py)

• Basic usage with direct LLM exception diagnosis
• Shows how to set up the diagnoser
• Demonstrates basic error handling and diagnosis

2. FastAPI Integration (examples/fastapi_example.py)

• Shows how to integrate LLM Catcher with FastAPI
• Demonstrates how to handle exceptions in FastAPI
• Includes a Swagger UI endpoint for testing

Run any example like this:

# Run minimal example
python examples/minimal_example.py

# Run FastAPI example
python examples/fastapi_example.py
# Then visit http://localhost:8000/docs

Each example includes detailed comments and demonstrates best practices for using LLM Catcher in different contexts.

Configuration

All settings can be configured through environment variables or the Settings class:

Complete Settings Example

from llm_catcher.settings import get_settings

settings = get_settings()

# Required
settings.openai_api_key = "your-api-key"  # Or set via LLM_CATCHER_OPENAI_API_KEY

# Model Settings
settings.llm_model = "gpt-4"  # Options: "gpt-4", "gpt-3.5-turbo", "gpt-4-1106-preview"
settings.temperature = 0.2  # Range: 0.0-1.0

Environment Variables

The same settings can be configured via environment variables:

# Required
LLM_CATCHER_OPENAI_API_KEY=your-api-key-here

# Model Settings
LLM_CATCHER_LLM_MODEL=gpt-4
LLM_CATCHER_TEMPERATURE=0.2

Settings Reference

Setting	Type	Default	Description
openai_api_key	str	None	Your OpenAI API key (required)
llm_model	str	"gpt-4"	LLM model to use
temperature	float	0.2	Model temperature (0.0-1.0)

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/yourusername/llm-catcher.git
cd llm-catcher

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

# Install development dependencies
pip install -e ".[dev]"  # This installs the package with development extras

Notes

• API key is required and must be provided via environment or settings
• Settings are validated on initialization
• Stack traces are included in LLM prompts for better diagnosis

License

MIT License